The amount of data in the world continues to grow at an incredible rate. IDC suggests that, by 2025, there will be 163ZB (zettabytes) of data – up from 4.4ZB in 2013. This massive increase in data is both a challenge and an opportunity for businesses. Contained within that data is the insight needed to improve decisions, enhance productivity and drive innovation. Big data analytics is the key to unlocking the information held within a company’s data.
To understand exactly what big data analytics is, it’s worth splitting the phrase into two parts: “big data” and “analytics.”
Big data can be described as the enormous amounts of information that organizations have gained access to over the last decade or so. Due to technological advances, it’s now far cheaper and easier to collect data by the terabyte or more, from a wider range of sources – not just traditional business applications but social media, support chats, digital imagery, the Internet of Things, and many more. These vast collections of data can reveal all sorts of insights.
“Analytics” refers to the challenge of finding the insight and information in the data quickly and efficiently enough to drive decision-making and identify new opportunities.
Big data analytics can handle both structured data – from the likes of database records and unstructured data – such as emails, Word documents or social media posts. The best big data analytics tools bring all this data together into a large data repository – often referred to as a “data lake” – where the data can be normalized to enable a range of analytics activities to take place.
Most big data analytics solutions include a number of data mining and data analysis tools. They support multiple analytics methods including decision tree analysis, clustering analysis, forecasting analytics, propensity analytics and sentiment analytics. Often big data analytics tools have segmentation software to split groups – such as customer records into smaller segments for more precise analysis.
The need for big data analytics
The concept behind big data analytics is not new. Companies have been analyzing their data for many years but the tools they used were not designed for the vast growth in the volume and types of new data being created. The need is underpinned by the four key characteristics of big data – often called the “4 V’s of big data.”
1. Volume
What makes big data big is its sheer volume. The cost of storage has reduced significantly but many organizations are looking for cloud-based big data analytics solutions that offer cheaper and more flexible storage capacity, as well as near limitless scalability to accommodate the huge data lakes on which to apply their analytical tools.
2. Variety
There are now more types of data than ever before. Traditionally, companies only had to be concerned with data created in their corporate systems. This was mainly structured data – data that follows the same structure for each record that would fit neatly into relational databases or spreadsheets.
Today, they also need to accommodate data from external sources, such as social media, mobile devices and, increasingly, Internet of Things (IoT) sensors and devices. Much of this data is unstructured – it does not conform to set formats in the way that structured data does. This makes unstructured data, such as text, much more difficult to capture and analyze.
A big data analytics tool can work with structured and unstructured data to discern patterns and trends in the data that would be impossible to do using the previous generation of data tools.
3. Velocity
Velocity is the frequency of incoming data that needs to be processed. For example, think of all the calls, texts, and Facebook status updates a telecom provider has to handle every day. And it’s not just limited to phones – everyday devices from cars to refrigerators are now data generators. According to Forbes, every Ford GT has 50 IoT sensors and 28 microprocessors that are capable of generating up to 100GB of data per hour. This requires a high-performance, secure platform to cope with this flow of data.
4. Visibility
Big data analytics, as it developed, hit a barrier. This type of tool had trouble interpreting information coming from the volumes of data collected. It would take a data scientist to make sense of the findings. The problem is that data scientists are scarce and expensive. The results of the analysis had to be made visible to everyone who needed it, in a format they understood. The best big data analytics platforms employ easy to use analytics dashboards to deliver insight to the right people, and an ability to create their own dashboards to gain top-level insight and drill down into the analysis as required.
Taken together, these capabilities provide an advanced analytics platform that can derive actionable insight from big data – a true big data analytics tool.
Types of Big Data Analytics
Given the broad scope outlined above, it should be no surprise that there are many forms of big data analytics. These include:
Descriptive Analytics
Descriptive analytics tools – sometimes called business intelligence – look at historical data within the organization to tell you what happened. They create simple reports, visualizations and decision trees that show what occurred at a particular point in time or over a period of time. In the larger landscape of big data analytics, these have basic but essential analytical functionality that performs an important function in helping improve performance and forecasting analysis.
Diagnostic Analytics
Diagnostic tools explain why something happened. More advanced than descriptive reporting tools, they allow a deep-dive into the historical data, apply big data modelling, and determine the root causes for a given situation.
Predictive Analytics
Predictive analytics uses data, analytical algorithms and machine learning techniques to identify the likelihood of prospective outcomes based on historical data. It identifies patterns and trends within the data that suggest how machines, parts and people will behave in the future.
Prescriptive Analytics
Prescriptive analytics is designed to find and recommend the best course of action for a given situation. It builds on the predictive function by showing the implications of each option and identifying the optimum course of action in real time.
AI-powered Analytics
AI-powered analytics – sometime called cognitive computing – provides “human-like” intelligence to understanding the context in vast amounts of structured and unstructured data. It combines advanced analytics capabilities with comprehensive Artificial Intelligence features – such as deep learning, machine learning and natural language recognition – to uncover the patterns that are contained in information.
An AI-powered analytics solution – such as OpenText™ Magellan™ – will get smarter and more accurate over time as it continually learns from the more interactions it has with data. A deep understanding of the information within an enterprise enables the business to draw from the wide variety of data sources to improve the quality of enterprise knowledge and implement real-time decision-making.
Why choose OpenText for big data analytics?
OpenText™ is at the forefront of developments in Business Intelligence, big data analytics and AI-powered analytics.
OpenText Magellan is a comprehensive AI-powered analytics platform that builds on the Analytics Suite with state-of-the-art machine learning and text-mining capacities. Magellan unlocks the value of EIM data to help you understand and analyze customers, trading partners, employees, orders, invoices, cases, documents and other data.
Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.
